Head of AI Engineering & Cyber Solutions

1.      Lead AI Engineering & Solution Delivery

• Define and execute the AI strategy across security, risk, and cyber enablement
• Build and scale the AI Hub in India as a global centre of excellence
• Own the design, development, and delivery of AI-enabled platforms, tools, and applications for cybersecurity teams.
• Build robust software engineering practices across architecture, development, testing, release management, DevSecOps, and production support.
• Lead engineering teams across AI engineers, software developers, data engineers, and automation specialists.
• Translate business and cyber requirements into scalable, maintainable, production-grade technology solutions.
• Progressively take ownership of broader IT development delivery for the team, beyond AI-only use cases.

• Partner with global security, data, and technology leadership on AI initiatives

2. Software Engineering & Technology Delivery

• Strong foundation in software architecture, application development, APIs, microservices, cloud-native engineering, and enterprise integration.
• Experience with modern engineering practices including Agile delivery, CI/CD, DevSecOps, automated testing, observability, secure coding, and production support.
• Ability to lead end-to-end solution delivery from problem framing and design to build, deployment, adoption, and run.
• Comfortable managing developers, AI engineers, data engineers, architects, and technical leads.

3. AI for Cyber (AI Enablement & Use Cases)

• Drive development and scaling of AI use cases for cybersecurity, including:
  • Security operations, threat detection, vulnerability management, AppSec, automation, reporting
• Translate cybersecurity problems into AI-driven solutions
• Work closely with AI engineers, data scientists, and cyber teams to deliver practical outcomes
• Ensure use cases are secure, scalable, and production-ready

4. Cyber for AI (AI Security & Risk)

• Establish and drive security practices for:
  • AI/ML pipelines, GenAI / LLM systems, and AI-enabled platforms and applications
  • Azure / AWS AI components and all relevant mitigations (esp guardrails)
  • Microsoft agentic ecosystem (M365 copilot, studio, github)
  • Data/ AI converged platforms (eg Databricks, Snowflake)
• Oversee cybersecurity and risk assessment of AI use cases across the lifecycle
• Define controls, standards, and frameworks for secure AI adoption
• Ensure alignment with enterprise governance, risk, and regulatory expectations
• Define, set and maintain appropriate controls directly embedding in the solution and/or leveraging on AI SPM solutions

5. Hands-on Technical Leadership

·       Act as a credible engineering leader, able to guide architecture, software design, platform choices, delivery quality, and production readiness.

·       Bring strong experience in modern application development, APIs, cloud-native engineering, data platforms, DevSecOps, automation, and AI/ML engineering.

·       Ensure AI solutions are not only innovative but also secure, scalable, maintainable, observable, and integrated into enterprise technology standards.

·       Challenge technology decisions and improve engineering maturity across the team.

• Bring working knowledge of:
  • AI/ML/GenAI architectures (including Agentic, MCP, AI gateways, RAG, LLM, CSP AI services, AI framework such as langchain / langsmith, giskar,  gandalf, security guardrails)
  • data pipelines and model lifecycle
  • AI risks and mitigation strategies

6. Governance & Operating Model

• Embed AI into enterprise risk and governance frameworks
• Define structured processes for:
  • AI use-case assessment
  • prioritization and validation
  • risk visibility and reporting
• Support leadership on regulatory readiness, controls, and audit expectations

7. Build & Lead a High-Impact Team

• Build and